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@inproceedings{blanc_conditional_2009,
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@article{blanc_confidence_2012,
	title = {Confidence regions for statistical model based shape prediction from sparse observations},
	issn = {{1558-254X}},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/22374354},
	doi = {10.1109/TMI.2012.2188904},
	abstract = {Shape prediction from sparse observation is of increasing interest in minimally invasive surgery, in particular when the target is not directly visible on images. This can be caused by a limited field of view of the imaging device, missing contrast or an insufficient signal-to-noise ratio. In such situations, a statistical shape model can be employed to estimate the location of unseen parts of the organ of interest from the observation and identification of the visible parts. However, the quantification of the reliability of such a prediction can be crucial for patient safety. We present here a framework for the estimation of complete shapes and of the associated uncertainties. This paper formalizes and extends previous work in the area by taking into account and incorporating the major sources of uncertainties, in particular the estimation of pose together with shape parameters, as well as the identification of correspondences between the sparse observation and the model. We evaluate our methodology on a large database of 171 human femurs and synthetic experiments based on a liver model. The experiments show that informative and reliable confidence regions can be estimated by the proposed approach.},
	journal = {{IEEE} Transactions on Medical Imaging},
	author = {Blanc, R and Sz{\'e}kely, G},
	month = feb,
	year = {2012},
	note = {{PMID:} 22374354}
},

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	year = {2009}
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@article{cootes_use_1994,
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@article{luthi_using_2011,
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@article{blanc_statistical_2011,
	title = {Statistical model based shape prediction from a combination of direct observations and various surrogates: Application to orthopaedic research},
	issn = {1361-8415},
	shorttitle = {Statistical model based shape prediction from a combination of direct observations and various surrogates},
	url = {http://www.sciencedirect.com/science/article/pii/S1361841512000503},
	doi = {10.1016/j.media.2012.04.004},
	abstract = {In computer-assisted orthopaedic surgery, recovering three-dimensional patient-specific anatomy from incomplete information has been focus of interest due to several factors such as less invasive surgical procedures, reduced radiation doses, and rapid intra-operative updates of the anatomy. The aim of this paper is to report results obtained combining statistical shape modeling and multivariate regression techniques for predicting bone shape from clinically and surgically relevant predictors, including sparse observations of the bone surface but also morphometric and anthropometric information. Different state of the art methods such as partial least square regression, principal component regression, canonical correlation analysis, and non-parametric kernel-based regression are compared. Clinically relevant surrogate variables and combinations are investigated on a database of 142 femur and 154 tibia shapes obtained from {CT} images. The results are evaluated using cross validation to quantify the prediction error. The proposed approach enables to characterize the added value of different predictors in a quantitative and localized fashion. Results indicate that complementary sources of information can be efficiently exploited to improve the accuracy of shape prediction.},
	number = {0},
        year = 2012,
	journal = {Medical Image Analysis},
	author = {Blanc, Rémi and Seiler, Christof and Székely, Gabor and Nolte, {Lutz-Peter} and Reyes, Mauricio},
	keywords = {Computer-assisted orthopaedic research, Linear regression, Shape prediction},
	file = {ScienceDirect Full Text PDF:/home/luethi/.zotero/zotero/0u7709nx.default/zotero/storage/RRG6U2T7/Blanc et al. - Statistical model based shape prediction from a co.pdf:application/pdf;ScienceDirect Snapshot:/home/luethi/.zotero/zotero/0u7709nx.default/zotero/storage/MGK94TP5/S1361841512000503.html:text/html}
}